Physical-informed deep learning framework for CO2-injected EOR compositional simulation
CO 2 injection for enhanced oil recovery (CO 2-EOR) is one of the industry's most widely
applied techniques for CO 2 utilization. People use the compositional simulation technique …
applied techniques for CO 2 utilization. People use the compositional simulation technique …
Dual-stream Representation Fusion Learning for accurate medical image segmentation
Accurate segmenting regions of interest in various medical images are essential to clinical
research and applications. Although deep learning-based methods have achieved good …
research and applications. Although deep learning-based methods have achieved good …
Hierarchical Optimization for Personalized Hand and Wrist Musculoskeletal Modeling and Motion Estimation
Objective: Surface electromyography (sEMG) driven musculoskeletal models are promising
to be applied in the field of human-computer interaction. However, due to the individual …
to be applied in the field of human-computer interaction. However, due to the individual …
Fault diagnosis of bearings using a two-stage transfer alignment approach with semantic consistency and entropy loss
Fault diagnosis (FD) of bearings is a research area with great relevance to industrial
applications. Also, multi-source domain adaptation-based FD methods have achieved more …
applications. Also, multi-source domain adaptation-based FD methods have achieved more …
A multi-resolution physics-informed recurrent neural network: formulation and application to musculoskeletal systems
This work presents a multi-resolution physics-informed recurrent neural network (MR PI-
RNN), for simultaneous prediction of musculoskeletal (MSK) motion and parameter …
RNN), for simultaneous prediction of musculoskeletal (MSK) motion and parameter …
Towards robust and efficient musculoskeletal modelling using distributed physics-informed deep learning
This article develops a novel distributed framework based on physics-informed deep
learning for robust and efficient musculoskeletal modeling in nonstationary scenarios, which …
learning for robust and efficient musculoskeletal modeling in nonstationary scenarios, which …
[HTML][HTML] SPU-BERT: Faster human multi-trajectory prediction from socio-physical understanding of BERT
Accurately predicting pedestrian trajectories requires a human-like socio-physical
understanding of movement, nearby pedestrians, and obstacles. However, traditional …
understanding of movement, nearby pedestrians, and obstacles. However, traditional …
Continuous Motion Intention Prediction Using sEMG for Upper-Limb Rehabilitation: A Systematic Review of Model-Based and Model-Free Approaches
Upper limb functional impairments persisting after stroke significantly affect patients' quality
of life. Precise adjustment of robotic assistance levels based on patients' motion intentions …
of life. Precise adjustment of robotic assistance levels based on patients' motion intentions …
A Physics-Informed Low-Shot Adversarial Learning For sEMG-Based Estimation of Muscle Force and Joint Kinematics
Y Shi, S Ma, Y Zhao, C Shi… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Muscle force and joint kinematics estimation from surface electromyography (sEMG) are
essential for real-time biomechanical analysis of the dynamic interplay among neural …
essential for real-time biomechanical analysis of the dynamic interplay among neural …
A Knowledge Transfer-based Personalized Human–Robot Interaction Control Method for Lower Limb Exoskeletons
M Yang, D Tian, F Li, Z Chen, Y Zhu… - IEEE Sensors …, 2024 - ieeexplore.ieee.org
Accurate intent recognition by patients while wearing exoskeletons is crucial during their
rehabilitation exercises. In this article, a transfer learning framework for human-robot …
rehabilitation exercises. In this article, a transfer learning framework for human-robot …